Multi-granular rigs for distributed node-based interactive workflows

ABSTRACT

Systems and methods are described for leveraging distributed computation systems to split animation application processing of node graphs into two components: 1) a low complexity, primary node graph, that is evaluated by the application of the local device; and 2) one or more, higher complexity, companion node graphs that connect to the primary node graph, and are evaluated by a distributed computation system. As the local device evaluates the original, low complexity node graph, an artist is provided with fast, direct manipulation of an animated object. At the same time, the distributed computation system evaluates the higher complexity, companion node graphs, thereby providing a user of the local application with higher fidelity versions of the primary node graph as they are computed.

TECHNICAL FIELD

The present disclosure relates generally to distributed computingtechniques for animating rigs, and more particularly, some embodimentsrelate to systems and methods for using multi-granular rigs indistributed node-based interactive workflows.

DESCRIPTION OF THE RELATED ART

Skeletal animation is a computer animation technique that represents anobject in two primary parts: 1) a mesh that is a surface representationof the object, and 2) a set of interconnected joints (i.e., a rig) usedto animate the mesh. Generally, a joint may refer to a bone and thepivot about which it rotates, or a joint may just refer to a pivot.Joints may be related in a hierarchy by joint transformations. Skeletalanimation may be applied to transform any object that implements thismesh and rig structure. For example, it may be applied to a whole or apart (e.g., arm, leg, hand, foot, torso) of an animated character,creature, or similar object. Alternatively, it may be applied toanimations of other objects, such as clothing, a door, a fork, etc.

During animation, a rig may be represented as a set of nodes of a nodegraph (e.g., a directed acyclic graph), each node having correspondinginput and output attributes. The attributes of a node describe its data,with each node type having a different set of attributes. For example,the nodes associated with an animating sphere may comprise attributessuch as radius, angular momentum, position, color, and transparency.During animation of a rig, all or a subset of the rig's nodes areevaluated as data flows along the connections in its graph. While thenode graph is evaluated, its data is visually rendered as a static imageor an animated scene on a display via a viewport.

Animation rigs used in film productions are extremely complex. In manycases, these rigs comprise tens of thousands of nodes, with each nodehaving many input attributes and/or computationally expensiveoperations. Such rigs use all of these nodes to generate appealing shapedeformations on computer-generated characters.

Currently, resource intensive node-based graphical software applicationsthat process complex node graphs are relegated to high-performancedesktop computers. Laptops, tablets, and other mobile devices generallylack the resources to run these applications efficiently. Moreover, evenhigh-performance desktop systems may take considerable time to processnode graphs of complex animated scenes, developed on softwareapplications such as Nuke®, Maya®, and Houdini®.

BRIEF SUMMARY OF THE DISCLOSURE

Embodiments of the technology disclosed herein leverage distributedcomputation systems to split animation application processing of nodegraphs into two components: 1) a low complexity, primary node graph,that is evaluated by the application of the local device; and 2) one ormore, higher complexity, companion node graphs that connect to theprimary node graph, and are evaluated by a distributed computationsystem.

In a first embodiment, a method, includes: spawning within a serversystem a server process that communicates over a network; receivingfirst node graph data, second node graph data, and set of inputattributes at the server system, where the second node graph is acompanion node graph of the first node graph; processing the second nodegraph data at the server process based on the set of input attributes;and transmitting the processed second node graph data to a computingdevice. In various implementations, the first node graph and the secondnode graph may be associated with a rigged object, and the second nodegraph data may be processed to render a higher fidelity representationof all or a subset of the rigged object. In further implementations, thefirst node graph may instantiate the second node graph.

In one implementation, the method further includes: receiving third nodegraph data at the server system, where the third node graph is acompanion node graph of the first node graph, and the third node graphhas a higher complexity than the second node graph; processing the thirdnode graph data at the server process based on the set of inputattributes; and transmitting the processed third node graph data to thecomputing device. The second node graph and the third node graph may behierarchical.

In one implementation, the method further includes: receiving theprocessed second node graph data at the computing device; andevaluating, at the computing device, a portion of the first node graphbased on the processed second node graph data. In this implementation,the server processed second node graph data may be transmitted to thecomputing device in response to a request initiated at an applicationrunning on the computing device.

In a second embodiment, a method includes: initiating a node graphapplication instance at a computing device, the node graph applicationinstance displaying an object comprising a rig; processing first nodegraph data of the rig at the application based on input attributesreceived at the computing device; initiating a request at the computingdevice for a server system to process second node graph data of the rigbased on the input attributes, where the second node graph data is ahigher complexity representation of the first node graph data; andreceiving the server system processed second node graph data at aviewport in the application. In one implementation, the method furtherincludes: receiving the processed first node graph data at the viewportof the application, and the step of receiving the server systemprocessed second node graph data at the viewport comprises replacing theprocessed first node graph data with the processed second node graphdata at the viewport.

As used herein to refer to a node graph, the term “complexity” generallyrefers to an amount of time required to process all nodes of the nodegraph by a computational system. A second node graph that is a “highercomplexity” than a first node graph will take longer to be processed bythe computational system than the first node graph. The complexity of anode graph may depend on the number of nodes in the graph, the number ofconnections in the graph, and the time and space requirements of eachnode. Other contributing factors include the complexity of operationsand the availability of resources.

As used herein, “companion node graph data” refers either to a nodegraph data that is a higher complexity representation of all or a subsetof a first set of node graph data; or to a node graph data that isevaluated in addition to the first set of node graph data to provide ahigher fidelity representation of the first set of node graph data.

As used herein, a “rig control” generally refers to a control forcontrolling how a node graph evaluates and deforms an animated object. Arig control may be used to define a shape, position, color, texture, orother property of an animated object or a part of an animated object.For example, for an animated character, a rig control may specify aposition of a hand, a shoulder rotation, an amount an eye closes, etc.During animation, an animator may manipulate a rig control to interactwith an animated object.

A rig control is associated with one or more rig control values. Forinstance, one rig control may have rig control values for the location,scale, and orientation of a character's hand. A rig control value mayassume a wide variety of variable or value types, such as, for example,a floating point variable, an integer variable, a boolean variable, anenumerated variable type, a percentage, a clamped range, etc.

As used herein, a “pose” generally refers to a particular combination orconfiguration of rig control values corresponding to a rig of ananimated object. For example, for an animated character comprising 3000rig control values, a pose may comprise 3000 values that define anoutput shape of the character.

As used herein, a “shape” of an animated object refers to a form andcharacteristic appearance of an animated object. A shape may berepresented as a mesh, a nurb, a curve, etc.

Other features and aspects of the disclosed method and system willbecome apparent from the following detailed description, taken inconjunction with the accompanying drawings, which illustrate, by way ofexample, the features in accordance with embodiments of the disclosure.The summary is not intended to limit the scope of the claimeddisclosure, which is defined solely by the claims attached hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure, in accordance with one or more variousembodiments, is described in detail with reference to the followingfigures. The figures are provided for purposes of illustration only andmerely depict typical or example embodiments of the disclosure.

FIG. 1A illustrates a conventional desktop environment in which nodebased graphical applications are processed.

FIG. 1B illustrates a conventional desktop environment in which nodebased graphical applications are processed.

FIG. 2 illustrates one example of a primary node graph connected tocompanion node graphs in accordance with the disclosure.

FIG. 3 illustrates one example of a distributed computing communicationsystem for processing node based graphical applications.

FIG. 4 illustrates an exemplary embodiment of a node based graphicalworkflow in accordance with the distributed computing communicationsystem of FIG. 3.

FIG. 5 illustrates a computing device side method of processing nodegraph data and input attributes.

FIG. 6 illustrates a server side method of processing node graph data,including companion node graph data, and input attributes received fromone or more computing devices.

FIG. 7 illustrates an example computing module that may be used toimplement various features of the system and methods disclosed herein.

The figures are not exhaustive and do not limit the disclosure to theprecise form disclosed.

DETAILED DESCRIPTION OF THE EMBODIMENTS OF THE DISCLOSURE

Before describing the disclosed method and system in detail, it isuseful to describe a conventional desktop environment in whichnode-based graphical applications are processed. FIG. 1A illustrates theconventional desktop environment. Desktop workstation 100 initiates anode graph application instance 110 to process node graph data (i.e.,one or more nodes and their corresponding connectivity in a node graph).In this example environment, the node graph comprises six nodes. Eachdepicted node may comprise a single node or a group of nodes. Theprocessed node graph data is visually rendered as a static image oranimation scene on the monitor of workstation 100 via viewport 115. Theuser of workstation 100 can modify the input attributes 111 of nodes inthe node graph by manipulating objects in viewport 115 or by directlyupdating the input attributes 111 of a node 114 in the node graph. Forexample, a user may modify rig control values during interactivemanipulation of an animated character.

The attributes of a node describe the data that the node stores andedits. For example, the nodes associated with an animation sphere maycomprise of attributes such as radius, angular momentum, position,color, and transparency. Groups of nodes 113 may share input attributes111, or alternatively, multiple nodes can have their own sets of inputattributes. In this desktop environment, input attributes may bemodified at any node in the graph. Once the input attributes 111 aremodified, workstation 100 begins evaluating the node graph in sequence.In this example, Node 1 receives input attributes at input 112. Afterinput attributes 111 are processed at node 1, node 1 output 113 passesoutput attributes to node 2's input. Node 4's input 112 waits for inputdata from the outputs of Node 2 and Node 3. This process continues untilthe data is processed at node 6, the final node in the graph. Thefully-evaluated node graph data is visually rendered through viewport115.

Each time an input to node graph is modified or a call to evaluate thenode graph is executed, this evaluation process repeats by passing onlydata that needs to be recalculated. Node graph applications accomplishthis process efficiently by flagging a node's inputs 112 and outputs113. When an input value 112 is changed, the node's dependent output 113is flagged. Any connection to the flagged output 113 is also flagged andthis propagation continues until the last possible node is reached. Asillustrated in FIG. 1B, when the node graph application requests a nodegraph re-evaluation, the node graph application checks if a node'soutputs are flagged, starting with the last or terminal node of the nodegraph. For example, if Node 6 determines it is flagged at 116, it isinstructed to evaluate itself. When that node evaluates, it sees thatits inputs are flagged and asks any connected input node at 117 toreevaluate itself. As the inputs and outputs of the nodes are evaluated,the inputs and outputs are unflagged. This process continues until thecritical data in the node graph is re-evaluated, and the inputs andoutputs are no longer flagged. In this manner, only flagged nodes areevaluated.

In the desktop environment of FIGS. 1A-1B, all node graph data isprocessed locally on workstation 100. As the complexity of the nodegraph increases, the speed at which the node graph data in applicationinstance 110 is evaluated and rendered on viewport 115 is limitedprimarily by the processing capabilities of workstation 100. In thisdesktop environment, the processing power of the user's workstation 100is the bottleneck for the efficient processing of complex node graphs ofanimation scenes.

Embodiments of the technology disclosed herein leverage distributedcomputation systems to split animation application processing of nodegraphs into two components: 1) a low complexity, primary node graph,that is evaluated by the application of the local device; and 2) one ormore, higher complexity, companion node graphs that connect to theprimary node graph, and are evaluated by a distributed computationsystem. As the local device evaluates the original, low complexity nodegraph, an artist is provided with fast, direct manipulation of ananimated object. At the same time, the distributed computation systemevaluates the higher complexity, companion node graphs, therebyproviding a user of the local application with higher fidelity versionsof the primary node graph as they are computed.

For example, consider an animator manipulating the arm controls of arigged character rendered on a viewport. The rigged character may beassociated with a primary node graph, a subset of which is used toevaluate the arm of the character. The subset of the primary node graphused to evaluate the arm may be connected to a companion node graph thatmay be evaluated to provide a higher fidelity representation of therigged character's arm. As the rig control values are modified, theprimary node graph for the character's arm is evaluated by theanimator's local device. Additionally, a request may be initiated at theanimator's local device for a server system to evaluate the companionnode graph. Upon evaluation of the primary node graph by the localdevice, a low fidelity representation of the arm may be rendered ordrawn on the viewport. Upon the server system's evaluation of thecompanion node graph, the evaluated companion node graph may be sentback to the local device to display a higher fidelity representation ofthe arm.

FIG. 2 illustrates one example of a primary node graph 160 connected tocompanion node graphs 170 and 180 in accordance with the disclosure. Asnoted above, a companion node graph is a higher complexityrepresentation of all or a subset of another node graph. Most often, theinitial inputs and final outputs of the two graphs are similar, so as tomake the two graphs interchangeable. However, the companion node graphis free to have additional inputs and outputs. Companion node graphs 170and 180, in this example, are respectively higher complexityrepresentations of node 162 and node group 164-165 of primary node graph160. For example, if evaluation of node 162 performs a single rigidtransformation (e.g., rotation or translation) of all of a riggedcharacter's toes, evaluation of companion node graph 170 may perform aseparate rigid transformation for each of the rigged character's toes.As another example, if evaluation of nodes 164-165 performs linearskinning of a rigged character's arm, evaluation of companion node graph180 may perform dual quaternion skinning of the rigged character's arm.

It should be noted that although companion node graphs 170 and 180 areillustrated in this example as having a greater number of nodes thanrespective node groups 162 and 164-165, the higher complexity ofcompanion node graphs 170 and 180 may be due to other factors. Forexample, node graphs 170 and 180 may have a greater number of nodeconnections, involve greater processing per node, greater resourceconsumption, greater hardware requirements, etc. Additionally, althoughcompanion node graphs 170 and 180 are illustrated in this example asreplacing respective node groups 162 and 164-165, companion node graphs170-180 may provide additional data that is evaluated in addition to thedata evaluated by node groups 162 and 164-165.

Node 162 and its corresponding companion node graph 170 receive, as aninput, the same output attributes from node 161, and send, as an output,input attributes to node 163. In embodiments, node graph 170 or node 162may use all or a subset of the attributes received from node 161 forevaluation. For example, as node graph 170 is higher complexity thannode 162, in some implementations node graph 170 may be evaluated usingall input attributes received from node 161 whereas node 162 may beevaluated using only a subset of these input attributes.

Nodes 164-165 and their corresponding companion node graph 180 receive,as an input, the same output attributes from node 163, and send, as anoutput, input attributes to node 166. In embodiments, node graph 180 ornode 164-165 may use all or a subset of the attributes received fromnode 163 for evaluation.

FIG. 3 illustrates one example of a distributed computing communicationsystem for distributed node based graphical workflows. Thecommunications system includes a plurality of computing devices 202-205,each communicating with other computing devices 202-205 and servers 201through communications network 200. Computing devices 202-205 maycomprise, but are not limited to, workstation, desktop, laptop,notebook, and tablet computers; hand-held devices such as tablets,smartphones, cellphones, and palmtops; and wearable devices such ashead-mounted displays. In communications network 200, the processing ofsome or all of the rigs (e.g., node based graphical data) generated byusers operating computing devices 202-205 is distributed over one ormore servers 201. In some embodiments, these servers perform gridcomputing.

As used herein, communications network 200 may refer to any network thatallows for a distributed computing system. Communications network 200may include a cloud computing network, an ad hoc network, an intranet,an extranet, a virtual private network (VPN), a local area network(LAN), a wireless LAN (WLAN), a wide area network (WAN), a cellulartelephone network, a satellite network, or any combination thereof.Communications network 200 may use a number of communication mediums.The communication medium may be a wired system, such as a coaxial cablesystem, a fiber optic cable system, an Ethernet cable system, or othersimilar communication medium. Alternatively, the communication mediummay be a wireless network system, such as a wireless personal areanetwork, a wireless local area network, a cellular network, or othersimilar communication medium.

In the communication system of FIG. 3, the results of evaluated nodegraphs (e.g., evaluated rigs), including primary node graphs andcompanion node graphs, may be cached in a shared cache 220. For example,given a set of rig controls and values defining a pose, that pose may beassociated with a shape determined by evaluating the rig (i.e., byevaluating a node graph and/or companion node graphs). Shared cache 220,in embodiments, may be distributed across multiple hosts (e.g., servers201 and/or devices 202-205) with the poses of past and future frames.Each host may pre-populate cache 220 with evaluated node graphs. In someinstances, the cached, evaluated node graphs may comprise differentfidelity representations of the same underlying object. For example,both low fidelity and high fidelity versions of a pose of a riggedcharacter or a subset of the rigged character may be cached. In thismanner, relying on cached pose data, a host may process a pose requestfor a rig during an animation session.

FIG. 4 illustrates an exemplary embodiment of a node based graphicalworkflow in accordance with the distributed computing communicationsystem of FIG. 3. Workstation 310 initiates node graph applicationinstance 300. The node graph 315 of application instance 300 is renderedon the monitor of workstation 300 via viewport 307. Node graph 315includes a primary node graph, including nodes 301, 302, 303, 304, and306, each node having respective inputs 308 and respective outputs 309.In addition, node graph 315 may include companion node graphs 351 and352. Each of companion node graphs 351-352 may provide a highercomplexity representation of all or a subset of the primary node graph.For instance, in the example of FIG. 4, each companion node graph351-352 may provide a higher complexity representation of nodes 303-304.

As an animator manipulates an animated object (e.g., by modifyingcontrols of a rigged character) using workstation 310, node graph 315 islocally evaluated to provide a first representation (e.g., a “low”fidelity representation) of the animated object. For example, dependingon the availability of computing resources for workstation 310, theprocessing speed of workstation 310, and other factors, workstation 310may evaluate the primary node graph (e.g., nodes 301-304 and 306) toprovide a “lightweight” or fast representation of a rigged characterthat an animator may manipulate with little to no latency.

In addition, as further described below, workstation 310 may initiate arequest (e.g., through application instance 300) for a server system(e.g., the distributed computation of FIG. 3) to process, based on theanimator's local manipulation of the animated object, companion nodegraphs 351-352 that provide a more complex representation of theanimated object. As the processed companion node graph data is madeavailable to workstation 310, the initial low fidelity representationdisplayed in the viewport may be replaced with progressively higherfidelity representations.

In some implementations, the rendered node graph data on viewport 307may also be pushed to the display of a mobile device 320, which is indirect communication 325 with desktop computer 310. Communication 325may be implemented wirelessly using any number of communicationprotocols, such as LTE or 802.11x wireless LAN protocols. Alternatively,the desktop computer may be in wired communication 325 with the mobiledevice. The wired connection may be implemented using a variety ofinterfaces, such as USB 2.0, USB 3.0, Thunderbolt, or other availableprotocols. Thus, the rendered node graph data may be pushed from thedesktop computer 310 to the mobile device 320 over wired or wirelesscommunication media.

With reference now to node graph application instance 300, node graph315 comprises five nodes 301, 302, 303, 304 and 306, companion nodegraphs 351-352, and one proxy node 305. A proxy node 305 is any computermodule that is used to moderate data flow of a node graph. When theviewport 307 refreshes or a request to reevaluate the node graph isinitiated, the proxy node 305 checks to see if its output 309 has beenflagged for reevaluation. If the proxy node 305 has been flagged forreevaluation, the proxy node 305 may initiate a request for serversystem 350 to have connected input nodes and their corresponding inputattributes processed in server 350. For example, the request may causeserver system to process connected companion node graphs 351-352, inputnodes 301-302, and their corresponding input attributes. In some cases,this request may be limited to nodes with flagged inputs. Proxy node 305may comprise locality metadata that specifies on what server the nodesare processed. Moreover, if bandwidth permits, proxy node 305 mayforward the data for local processing of companion node graphs 351 or352.

Proxy node 305 may also determine whether companion node graph data hasbeen previously processed and cached on the same LAN as the computingdevice, the computing device itself, or a storage medium attached to thecomputing device. If the companion node graph data has been cached, theproxy node 305 may return the cached data as its output 309 instead ofcalling the server application instance 340. In embodiments, a nodegraph 315 may comprise one or more proxy nodes 305 for receiving serverprocessed data of companion node graphs. In this manner, higher fidelityrepresentations of a primary node graph may be processed by a serversystem while an animator locally interacts with a lightweight version ofan animated rig (e.g., a primary node graph).

Server 350, which runs node graph application instance 340, receives acopy of all user inputs associated with the nodes preceding proxy node305 in the node graph (i.e., nodes 301-302). Server 350 comprises nodegraph 315′, which is a copy of node graph 315, and companion node graphs351′ and 352′ (i.e., copies of companion node graphs 351-352), which areevaluated according to the user inputs.

The placement of a proxy node 305 in the node graph may be determined bythe node graph application instance based on where companion node graphs351-352 receive input attributes from and send output attributes to theprimary node graph. The placement of proxy nodes 305 may be static suchthat proxy nodes are situated at fixed locations in the graph.Alternatively, proxy nodes 305 may be dynamically placed by computing agood position in the node graph for their placement.

In application instance 300, when a user manipulates objects in viewport307 or directly updates the input attributes of a node, the flaggedinputs 308 and outputs 309 propagate their state forward. For example,if the user modifies the input attributes of nodes 301 and/or 302, thenthe inputs 308 and outputs 309 of nodes 303, 304, 305, and 306 areflagged. In this example embodiment, server system 350 receives a copyof all user inputs associated with nodes preceding proxy node 305 in thenode graph—that is, nodes 301-302. When nodes 301-302 are flagged, theflagged inputs and outputs propagate their states forward to servernodes 301′, 302′. In addition, the final node of companion node graphs351′ or 352′ may signal proxy node 305 to re-evaluate. In otherembodiments, server 350 may maintain a copy of all user inputsassociated with all nodes in application instance 300. Once all inputs308 and outputs 309 have been flagged in the node graph 315, evaluationof the node graph 315 may be initiated automatically or when theviewport 307 refreshes.

Beginning with node 306, the node checks if its respective outputs 309have been flagged. If the outputs are flagged, node 306 is instructed toevaluate itself. When node 306 evaluates, it determines that itsrespective inputs 308 are flagged and instructs connected node 304 andproxy node 305 to evaluate itself. On the local side, this process mayrepeat upstream to nodes 303, and 301-302.

After determining that its respective inputs 308 are flagged, proxy node305 submits a request over communications network 200 to have allconnected nodes (i.e., companion node graphs 351 or 352) andcorresponding input attributes processed by server 350.

Server system 350, which receives a copy of all user inputs associatedwith nodes 301 and 302, runs node graph application instance 340, whichprocesses nodes 301′ and 302′ (copies of nodes 301 and 302) andcompanion node graphs 351-352 based on the saved user inputs. Server 350may process nodes 301′ and 302′ and companion node graphs 351-352 in thesame manner as any other node graph application instance. That is, theevaluation process visits only flagged nodes. In other embodiments,server 350 may process all nodes regardless of whether they have beenflagged.

In embodiments, server system 350 may process companion node graphs351-352 in a hierarchical fashion. For example, if evaluation ofcompanion node graph 351 results in a “medium” complexity representationof a rigged character's arm, and evaluation of companion node graph 352results in a “high” complexity representation of the rigged character'sarm, server system 350 may first process companion node graph 351followed by companion node graph 352. As the “medium” complexitycompanion node graph 351 is processed, its outputs may be used toprocess the higher complexity companion node graph 352. In this manner,the “high” complexity node graph may be computed with improvedefficiency or accuracy. Alternatively, companion node graphs 351 and 352may be processed in parallel and separately.

The outputs of the companion node graph data are transmitted overcommunications network 200 back to proxy node 305. After receipt of theoutput of the companion node graph, the viewport may be refreshed. Forexample, node 306 may be reevaluated and processed in applicationinstance 300 based on the input attributes received from proxy node 305,and the updated node graph may be rendered on viewport 307.

FIG. 5 describes a method for leveraging distributed computing forprocessing a node graph like the one in application instance 300. Atstep 401, workstation 310 initiates a node graph application instance300. In some implementations, the step of initiating a node graphapplication instance 300 may also comprise transmitting an instructionto a server 350 to initiate a node graph application instance comprisinga copy of the same nodes as application instance 300. At step 402, thecomputing device receives input attributes and/or node graphs from theuser of computing device 310, from storage on computing device 310, fromnetwork storage on communications network 200, or from some othersource. For example, a primary node graph, one or more companion nodegraphs, and a single set of input attributes may be received. Step 402may further comprise transmitting the node graphs and input attributesto server 350 to allow the server 350 to update its copy of nodes andassociated input attributes.

At step 403, processing of the primary node graph based on a set ofinput attributes is initiated either automatically or in response to arequest from the node graph application. For example, processing may beinitiated in response to an animator moving a rig control of an animatedcharacter. The primary node graph data may be locally processed (step405) and output to a viewport for display (step 407) until companionnode graph data has been received and is ready for output (step 409).

At step 404, a request may submitted to a server system to processcompanion node graph data based on the same set of input attributesreceived by the primary node graph. For example, the request may beinitiated at a proxy node in response to the same call that initiatedprimary node graph data processing. Prior to initiating the request, acheck may be performed to determine if the companion node graph data hasbeen previously processed and cached. If cached data is detected withinthe same LAN as workstation 310, workstation 310, or a storage mediumattached to workstation 310, the cached data is applied and the requestto the server system may be skipped.

In response to the request, a server remotely processes the companionnode graph data (step 406) and transmits the results back. At step 408,the processed companion node graph data is received. Prior to updatingthe viewport, the companion node graph data may be used to reevaluate aportion of the primary node graph. At step 409, once the receivedcompanion node graph data is ready for output, the viewport is updated.

In regards to the server side, FIG. 6 illustrates a server side methodof evaluating node graph data, including companion node graph data.Server 350 spawns a process (step 501) configured to receive and processnode graph data and input attributes. Each time the server receives nodegraph data and input attributes (step 502) from an instance of the nodegraph running on a client, the server updates the process to reflect thechanges to the process' node graph data and input attributes (step 503).

As illustrated in this example, server 350 receives input attributes512, primary node graph 510, and companion node graph data 511.Companion node graph data 511, when evaluated, may improve the fidelityof all or a subset of the results derived from an evaluation of primarynode graph 510. Companion node graph data may comprise a set of nodesthat are evaluated in place of nodes in primary node graph 510,additional data that is computed by primary node graph 510 (e.g.,additional nodes or node connections), or some combination thereof. Inone embodiment, primary node graph 510 may instantiate one or morecompanion node graphs during execution.

If the server receives a request to process companion node graph databased on the input attributes (step 504), the server begins to processthe data. For example, the server may receive a request from a proxynode that instructs the server to evaluate companion node graph databased on received input attributes. A computing device may initiate therequest for the proxy node when an animator manipulates a riggedcharacter on a viewport.

The server process determines if the companion node graph has previouslybeen evaluated and cached (step 505). If the node graph has not beenpreviously evaluated and cached, the companion node graph is evaluatedat the server based on the node graph input attributes (step 506).

The processed companion node graph is then cached (step 507) andtransmitted (step 508) to one or more computing devices overcommunications network 200. The server-processed graphs may betransmitted automatically or in response to a request initiated by anapplication running on a computing device.

In one embodiment, server 300 may evaluate a hierarchy of companion nodegraphs, where each companion node graph in the hierarchy represents anincreasing level of complexity. As companion node graphs are evaluatedin increasing order of complexity, they may be made available to therequesting proxy node. By way of example, consider a first companionnode graph, including 1000 nodes, that may be evaluated to provide a“medium” complexity representation of a character's leg, and a secondcompanion node graph, including 2000 nodes, that may be evaluated toprovide a “high” complexity representation of the same leg. In thisexample, server system 350 may first evaluate the first companion nodegraph and make the results available to the requesting proxy node.Server system 350 may then evaluate the second companion node graph andmake the results available to the same proxy node. In some embodiments,the outputs of the evaluation of the first companion node graph mayserve as inputs of the evaluation of the second companion node graph.

The server 350 may comprise a plurality of servers. These multipleservers perform grid-computing. Animation nodes, for example, may beevaluated in a distributed manner and then assembled into shots to bestreamed to a plurality of computing devices. Additionally, processingtasks may be moved between the local node graph application and theremote server instance. This ability to dynamically spread thecomputation of node graphs across local or remote compute devices, maybe live or on-demand. Moreover, node graph data stored on a server forreuse may be preloaded each time a server process is spawned.

The server 350 may be application agnostic (i.e. independent of the nodegraph application). For example, a server 350 may receive a plurality ofnode graphs for processing from a plurality of computing devices runningdifferent node graph applications such as Nuke®, Maya®, and Houdini®.Server 350 may comprise a computer module configured to providesyntactic and semantic interoperability. Semantic interoperability maybe provided among these different node graph applications using a commoninformation exchange reference model. For example, the server 350 mayreceive for processing node graph data from a computing device running aMaya® node graph application instance. Before transmitting the processednode graph data to a computing device running a Nuke node graphapplication instance, the server may translate the Maya® node graph datausing a common information exchange reference model.

Additionally, the server process may provide a management system totrack where data is computed, where it connects to in the primary nodegraph, where companion node graphs connect into the primary node graph,where companion node graphs connect into each other, who currently isediting/modifying the node graph, and whether or not the data is live oreditable.

FIG. 7 illustrates an example computing module that may be used toimplement various features of the methods disclosed herein.

As used herein, the term module might describe a given unit offunctionality that can be performed in accordance with one or moreembodiments of the present application. As used herein, a module mightbe implemented utilizing any form of hardware, software, or acombination thereof. For example, one or more processors, controllers,ASICs, PLAs, PALs, CPLDs, FPGAs, logical components, software routinesor other mechanisms might be implemented to make up a module. Inimplementation, the various modules described herein might beimplemented as discrete modules or the functions and features describedcan be shared in part or in total among one or more modules. In otherwords, as would be apparent to one of ordinary skill in the art afterreading this description, the various features and functionalitydescribed herein may be implemented in any given application and can beimplemented in one or more separate or shared modules in variouscombinations and permutations. Even though various features or elementsof functionality may be individually described or claimed as separatemodules, one of ordinary skill in the art will understand that thesefeatures and functionality can be shared among one or more commonsoftware and hardware elements, and such description shall not requireor imply that separate hardware or software components are used toimplement such features or functionality.

Where components or modules of the application are implemented in wholeor in part using software, in one embodiment, these software elementscan be implemented to operate with a computing or processing modulecapable of carrying out the functionality described with respectthereto. One such example computing module is shown in FIG. 7. Variousembodiments are described in terms of this example-computing module1000. After reading this description, it will become apparent to aperson skilled in the relevant art how to implement the applicationusing other computing modules or architectures.

Referring now to FIG. 7, computing module 1000 may represent, forexample, computing or processing capabilities found within desktop,laptop, notebook, and tablet computers; hand-held computing devices(tablets, PDA's, smart phones, cell phones, palmtops, etc.); mainframes,supercomputers, workstations or servers; or any other type ofspecial-purpose or general-purpose computing devices as may be desirableor appropriate for a given application or environment. Computing module1000 might also represent computing capabilities embedded within orotherwise available to a given device. For example, a computing modulemight be found in other electronic devices such as, for example, digitalcameras, navigation systems, cellular telephones, portable computingdevices, modems, routers, WAPs, terminals and other electronic devicesthat might include some form of processing capability.

Computing module 1000 might include, for example, one or moreprocessors, controllers, control modules, or other processing devices,such as a processor 1004. Processor 1004 might be implemented using ageneral-purpose or special-purpose processing engine such as, forexample, a microprocessor, controller, or other control logic. In theillustrated example, processor 1004 is connected to a bus 1002, althoughany communication medium can be used to facilitate interaction withother components of computing module 1000 or to communicate externally.

Computing module 1000 might also include one or more memory modules,simply referred to herein as main memory 1008. For example, preferablyrandom access memory (RAM) or other dynamic memory, might be used forstoring information and instructions to be executed by processor 1004.Main memory 1008 might also be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 1004. Computing module 1000 might likewise includea read only memory (“ROM”) or other static storage device coupled to bus1002 for storing static information and instructions for processor 1004.

The computing module 1000 might also include one or more various formsof information storage mechanism 1010, which might include, for example,a media drive 1012 and a storage unit interface 1020. The media drive1012 might include a drive or other mechanism to support fixed orremovable storage media 1014. For example, a hard disk drive, a solidstate drive, a magnetic tape drive, an optical disk drive, a CD or DVDdrive (R or RW), or other removable or fixed media drive might beprovided. Accordingly, storage media 1014 might include, for example, ahard disk, a solid state drive, magnetic tape, cartridge, optical disk,a CD, DVD, or Blu-ray, or other fixed or removable medium that is readby, written to or accessed by media drive 1012. As these examplesillustrate, the storage media 1014 can include a computer usable storagemedium having stored therein computer software or data.

In alternative embodiments, information storage mechanism 1010 mightinclude other similar instrumentalities for allowing computer programsor other instructions or data to be loaded into computing module 1000.Such instrumentalities might include, for example, a fixed or removablestorage unit 1022 and an interface 1020. Examples of such storage units1022 and interfaces 1020 can include a program cartridge and cartridgeinterface, a removable memory (for example, a flash memory or otherremovable memory module) and memory slot, a PCMCIA slot and card, andother fixed or removable storage units 1022 and interfaces 1020 thatallow software and data to be transferred from the storage unit 1022 tocomputing module 1000.

Computing module 1000 might also include a communications interface1024. Communications interface 1024 might be used to allow software anddata to be transferred between computing module 1000 and externaldevices. Examples of communications interface 1024 might include a modemor softmodem, a network interface (such as an Ethernet, networkinterface card, WiMedia, IEEE 802.XX or other interface), acommunications port (such as for example, a USB port, IR port, RS232port Bluetooth® interface, or other port), or other communicationsinterface. Software and data transferred via communications interface1024 might typically be carried on signals, which can be electronic,electromagnetic (which includes optical) or other signals capable ofbeing exchanged by a given communications interface 1024. These signalsmight be provided to communications interface 1024 via a channel 1028.This channel 1028 might carry signals and might be implemented using awired or wireless communication medium. Some examples of a channel mightinclude a phone line, a cellular link, an RF link, an optical link, anetwork interface, a local or wide area network, and other wired orwireless communications channels.

In this document, the terms “computer readable medium”, “computer usablemedium” and “computer program medium” are used to generally refer tonon-transitory media, volatile or non-volatile, such as, for example,memory 1008, storage unit 1022, and media 1014. These and other variousforms of computer program media or computer usable media may be involvedin carrying one or more sequences of one or more instructions to aprocessing device for execution. Such instructions embodied on themedium, are generally referred to as “computer program code” or a“computer program product” (which may be grouped in the form of computerprograms or other groupings). When executed, such instructions mightenable the computing module 1000 to perform features or functions of thepresent application as discussed herein.

Although described above in terms of various exemplary embodiments andimplementations, it should be understood that the various features,aspects and functionality described in one or more of the individualembodiments are not limited in their applicability to the particularembodiment with which they are described, but instead can be applied,alone or in various combinations, to one or more of the otherembodiments of the application, whether or not such embodiments aredescribed and whether or not such features are presented as being a partof a described embodiment. Thus, the breadth and scope of the presentapplication should not be limited by any of the above-describedexemplary embodiments.

Terms and phrases used in this document, and variations thereof, unlessotherwise expressly stated, should be construed as open ended as opposedto limiting. As examples of the foregoing: the term “including” shouldbe read as meaning “including, without limitation” or the like; the term“example” is used to provide exemplary instances of the item indiscussion, not an exhaustive or limiting list thereof; the terms “a” or“an” should be read as meaning “at least one,” “one or more” or thelike; and adjectives such as “conventional,” “traditional,” “normal,”“standard,” “known” and terms of similar meaning should not be construedas limiting the item described to a given time period or to an itemavailable as of a given time, but instead should be read to encompassconventional, traditional, normal, or standard technologies that may beavailable or known now or at any time in the future. Likewise, wherethis document refers to technologies that would be apparent or known toone of ordinary skill in the art, such technologies encompass thoseapparent or known to the skilled artisan now or at any time in thefuture.

The presence of broadening words and phrases such as “one or more,” “atleast,” “but not limited to” or other like phrases in some instancesshall not be read to mean that the narrower case is intended or requiredin instances where such broadening phrases may be absent. The use of theterm “module” does not imply that the components or functionalitydescribed or claimed as part of the module are all configured in acommon package. Indeed, any or all of the various components of amodule, whether control logic or other components, can be combined in asingle package or separately maintained and can further be distributedin multiple groupings or packages or across multiple locations.

Additionally, the various embodiments set forth herein are described interms of exemplary block diagrams, flow charts and other illustrations.As will become apparent to one of ordinary skill in the art afterreading this document, the illustrated embodiments and their variousalternatives can be implemented without confinement to the illustratedexamples. For example, block diagrams and their accompanying descriptionshould not be construed as mandating a particular architecture orconfiguration.

While various embodiments of the present disclosure have been describedabove, it should be understood that they have been presented by way ofexample only, and not of limitation. Likewise, the various diagrams maydepict an example architectural or other configuration for thedisclosure, which is done to aid in understanding the features andfunctionality that can be included in the disclosure. The disclosure isnot restricted to the illustrated example architectures orconfigurations, but the desired features can be implemented using avariety of alternative architectures and configurations. Indeed, it willbe apparent to one of skill in the art how alternative functional,logical or physical partitioning and configurations can be implementedto implement the desired features of the present disclosure. Also, amultitude of different constituent module names other than thosedepicted herein can be applied to the various partitions. Additionally,with regard to flow diagrams, operational descriptions and methodclaims, the order in which the steps are presented herein shall notmandate that various embodiments be implemented to perform the recitedfunctionality in the same order unless the context dictates otherwise.

What is claimed is:
 1. A method, comprising: spawning within a serversystem a server process that communicates over a network; receivingfirst node graph data, second node graph data, and set of inputattributes at the server system, wherein the second node graph is acompanion node graph of the first node graph; processing the second nodegraph data at the server process based on the set of input attributes;and transmitting the processed second node graph data to a computingdevice.
 2. The method of claim 1, further comprising: receiving thirdnode graph data at the server system, wherein the third node graph is acompanion node graph of the first node graph, wherein the third nodegraph has a higher complexity than the second node graph; processing thethird node graph data at the server process based on the set of inputattributes; and transmitting the processed third node graph data to thecomputing device.
 3. The method of claim 2, wherein the first node graphis associated with an animation rig.
 4. The method of claim 3, whereinthe third node graph is processed after the second node graph, whereinthe second node graph and third node graph are hierarchical.
 5. Themethod of claim 1, wherein the first node graph instantiates the secondnode graph.
 6. The method of claim 1, further comprising: caching theprocessed second node graph data, wherein processing the second nodegraph data and caching the processed second node graph data areperformed if the server process determines that it has not previouslyprocessed the second node graph data.
 7. The method of claim 1, whereinthe server processed second node graph data is transmitted to thecomputing device in response to a request initiated at an applicationrunning on the computing device.
 8. The method of claim 3, furthercomprising executing an instance of an application on the server systemthat can process the received node graph data and input attributes. 9.The method of claim 1, further comprising: receiving the processedsecond node graph data at the computing device; and evaluating, at thecomputing device, a portion of the first node graph based on theprocessed second node graph data.
 10. The method of claim 3, furthercomprising: receiving the processed second node graph data at thecomputing device; evaluating, at the computing device, a portion of thefirst node graph based on the processed second node graph data;receiving the processed third node graph data at the computing device;and evaluating, at the computing device, a portion of the first nodegraph based on the processed third node graph data.
 11. A systemcomprising: a server configured to: spawn a server process that runsinside of a grid computing instance that communicates over a network;receive first node graph data, second node graph data, and set of inputattributes, wherein the second node graph is a companion node graph ofthe first node graph, wherein the first node graph is associated withanimation rig; process the second node graph data at the server processbased on the set of input attributes; and transmit the processed secondnode graph data to a computing device; and the computing device, whereinthe computing device is configured to transmit the input attributes tothe server over the network.
 12. The system of claim 11, wherein theserver is further configured to: receive third node graph data, whereinthe third node graph is a companion node graph of the first node graph,wherein the third node graph has a higher complexity than the secondnode graph; process the third node graph data at the server processbased on the set of input attributes; and transmit the processed thirdnode graph data to the computing device.
 13. The system of claim 12,wherein the third node graph is processed after the second node graph,wherein the second node graph and third node graph are hierarchical. 14.The system of claim 1, wherein the first node graph instantiates thesecond node graph.
 15. The system of claim 11, wherein the serverprocessed second node graph data is transmitted to the computing devicein response to a request initiated at an application running on thecomputing device.
 16. The system of claim 11, wherein the computingdevice is configured to: receive the processed second node graph data;and evaluate a portion of the first node graph based on the processedsecond node graph data.
 17. The system of claim 12, wherein thecomputing device is configured to: receive the processed second nodegraph data; evaluate a portion of the first node graph based on theprocessed second node graph data; receive the processed third node graphdata; and evaluate a portion of the first node graph based on theprocessed third node graph data.
 18. A method, comprising: initiating anode graph application instance at a computing device, the node graphapplication instance displaying an object comprising a rig; processingfirst node graph data of the rig at the application based on inputattributes received at the computing device; initiating a request at thecomputing device for a server system to process second node graph dataof the rig based on the input attributes, wherein the second node graphdata is a higher complexity representation of the first node graph data;and receiving the server system processed second node graph data at aviewport in the application.
 19. The method of claim 18, furthercomprising: receiving the processed first node graph data at theviewport of the application, wherein the step of receiving the serversystem processed second node graph data at the viewport comprisesreplacing the processed first node graph data with the processed secondnode graph data at the viewport.
 20. The method of claim 18, furthercomprising: initiating a request at the computing device for a serversystem to process third node graph data of the rig based on the inputattributes, wherein the third node graph data is a higher complexityrepresentation of the second node graph data; and receiving the serversystem processed third node graph data at a viewport in the application.